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AWSCTD research papers

Attack-Caused Windows OS System Calls Traces Dataset

  • Čeponis, Dainius, and Nikolaj Goranin. "Towards a robust method of dataset generation of malicious activity for anomaly-based HIDS training and presentation of AWSCTD dataset." Baltic Journal of Modern Computing 6, no. 3 (2018): 217-234.

  • Goranin, Nikolaj, and Dainius Čeponis. "Investigation of AWSCTD dataset applicability for malware type classification." Security & Future 2, no. 2 (2018): 83-86.

  • Čeponis, Dainius, and Nikolaj Goranin. "Evaluation of Deep Learning Methods Efficiency for Malicious and Benign System Calls Classification on the AWSCTD." Security and Communication Networks 2019 (2019).

  • Čeponis, Dainius, and Nikolaj Goranin. "Investigation of Dual-Flow Deep Learning Models LSTM-FCN and GRU-FCN Efficiency against Single-Flow CNN Models for the Host-Based Intrusion and Malware Detection Task on Univariate Times Series Data." Applied Sciences 10.7 (2020): 2373.

  • Vyšniūnas, Tolvinas, et al. "Risk-Based System-Call Sequence Grouping Method for Malware Intrusion Detection." Electronics 13.1 (2024): 206.

Example of how to run training/testing:

python AWSCTD.py //full//path//to//MalwarePlusClean//010_2.csv AWSCTD-CNN-S

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